1. Field of the Invention
The present invention relates to a genetic robot, and more particularly to a method and an apparatus for learning behavior (i.e., actions) in a software robot among genetic robots.
2. Description of the Related Art
In general, a genetic robot refers either to an artificial creature having a genetic code of its own, to a software robot (i.e., a sobot), or to a general robot. Also, a robot genetic code signifies a single robot genome consisting of multiple artificial chromosomes. Herein, the software robot refers to an artificial creature having a software form which, transferring through a network, can now interact with a user as an independent software agent, and can again act as an intelligent unit of a robot that connects a hardware robot with a sensor network.
The multiple artificial chromosomes defined in the abovementioned software robot, interacting with an environment outside of the robot, define a change of internal states consisting of motivation, homeostasis, emotional states, etc., inside of the robot, and individuality or personality peculiar to the robot determining manifested behavior accompanied by the change of internal states. Herein, the definition of an artificial creature, motivation, homeostasis, emotions, behavior, and the like is as shown in TABLE 1.
TABLE 1artificialAn artificial creature acts on the motivation of a robot itself,creaturehas emotions, and can select its behavior, interacting with ahuman being in real time.individualityIt is not a simple and summarized technique of behavior,but a determiner of a part or the whole thereof, and may beconstrued as personality if it is thought of as a humanbeing. This concept includes motivation, homeostasis andemotions. Therefore, an individuality engine means anengine having all of motivation, homeostasis and emotions.It corresponds to a determiner that brings about variouskinds of internal states and behavior manifestations.motivationA process which causes a living body to arouse and keepactivities thereof, and to control the pattern of the activitiesthereof. It causes selecting and performing behavior. Forexample, curiosity, feelings of intimacy, boredom, evasivedesire, possessiveness, and the like.homeostasisA function which enables a living body to keep aphysiological state as an individual stable even though it isaffected by changes of external and internal environments.It causes selecting and performing behavior. For instance,hunger, sleepiness, fatigue, and the like.emotionsSubjective restlessness induced when a living body takes acertain behavior. For example, happiness, sadness, anger,fear, and the like.behaviorThe general term for an individual's actions, includingmoving to a specific spot, stopping, and the like. Forinstance, in the case of animals, sleeping, feeding, running,and the like. The number of kinds of actions that anindividual can select is limited, and in a certain instant,each individual can execute only one behavior.
In addition, the above artificial chromosome can be divided into genetic information related to essential elements, genetic information related to internal states, and genetic information related to behavior determination. Herein, the genetic information related to essential elements refers to essential parameters which have a great effect on the change of internal states and external behavior manifestation, and the genetic information related to internal states refers to parameters which affect internal states of a robot in relation to an external input applied to the robot. Furthermore, the genetic information related to behavior determination refers to parameters which determine external behavior related to the above internal states, depending on currently determined internal states.
Herein, the internal states refers to states such as motivation, homeostasis, emotions, and the like. Therefore, the internal states of the robot, as shown in TABLE 2, can be determined by respective internal states, and by parameters of internal states, depending on respective external stimuli, i.e., by the genetic information related to internal states.
TABLE 2internal statesmotivationa feelingexternalofa sense ofhomeostasisemotionsstimuliintimacy. . .hostilityhunger. . .sleepinesshappiness. . .sadnesspat80. . .−400. . .040. . .−20strike−30. . .500. . .0−30. . .30surprising0. . .50. . .010. . .0. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .pacifying40. . .−400. . .050. . .−50
The genetic information related to behavior determination can be represented in the same manner as in TABLE 2, provided that it includes various manifestable actions in substitution for the above external stimuli. Therefore, the genetic information related to behavior determination includes parameters related to specific actions in regard to respective internal states, i.e. parameters of internal states, such as motivation, homeostasis and emotions, the values of which enable respective actions to manifest themselves.
Also, essential parameters which have a great effect on each change of these internal states and on external behavior manifestation, may be represented as: whether it is volatile, an initial value, the mean value, the convergence value, attenuation values as time elapses, a specific value determined by a specific time, and the like. The genetic information related to essential elements can configure these essential parameters for a special purpose. Hence, this genetic information related to essential elements includes: respective internal states, i.e. motivation, homeostasis, whether it is volatile depending on internal states of emotions, an initial value, the mean value, the convergence value, attenuation values, a specific value, and so on.
In this case, a robot genome consists of the genetic information related to essential elements, the genetic information related to internal states, and the genetic information related to behavior determination. The genetic information related to essential elements is made up of parameters of internal states, and parameters of elements which are essential to a change of internal states corresponding to each internal state and to external behavior manifestation. The genetic information related to internal states is made up of parameters of various external stimuli, and parameters of internal states respectively corresponding to the external stimuli. The genetic information related to behavior determination is made up of parameters of various manifested actions, and parameters of internal states respectively corresponding to the manifested actions. Therefore, as shown in TABLE 3 below, the robot genome can be represented through a two-dimensional matrix as genetic information related to respective internal states, essential elements respectively corresponding to the internal states, external stimuli, and manifested actions.
TABLE 3internal statesmotivationa feelinga senseofofhomeostasisemotionsintimacy. . .hostilityhunger. . .sleepinesshappiness. . .sadnessessentialvolatilitya gene related toa gene related toa gene related toelementsan initialessential elementsessentialessential elementsvalue(motivation)elements(emotions). . .(homeostasis)attenuationvaluesexternalpata gene related toa gene related toa gene related tostimulistrikeinternal statesinternal statesinternal states. . .(motivation)(homeostasis)(emotions)pacifyingmanifestedlaughinga gene related toa gene related toa gene related tobehaviorlookingbehaviorbehaviorbehaviorarounddeterminationdeterminationdetermination. . .(motivation)(homeostasis)(emotions)rolling
Therefore, a current robot platform determines a specific manifested behavior based on current internal states, i.e. states such as motivation, homeostasis, emotions, and so on, and implements behavior accompanied by the determination. For example, if an internal state of a robot corresponds to a hungry state, the robot determines its behavior for importuning a man for something or other, and puts the determination into action. Accordingly, the robot can be embodied so as to act like an actual living being.
The software robot having characteristics as abovementioned should provide a user with services without restrictions on time and space in a ubiquitous environment. In order to freely transfer over a network, the software robot must have an IP address of a device whose transition is enabled, and exist in an apparatus which is employed presently. So as to interact with the user of the apparatus, the software robot can perform the same functions as those of a real creature, i.e. selecting behavior by itself, adapting itself to its environment, expressing its emotions, and the like.
In order to adapt the software robot to its environment, it should be taught how to behave itself. When the software robot shows its response to an object of interest, a user gives the software robot a reward (i.e., praise) or penalty (i.e., a scolding). By doing this, when the next object of interest approaches, an inclination, such as whether it avoids or approaches the object of interest, can be changed. This is called “preference learning.” The preference learning teaches the software robot its degree of preference corresponding to likes or dislikes to a certain object. For instance, if a user praises the software robot when it finds a yellow ball, by increasing happiness among emotional states and decreasing an evasive motivation state, its behavior and the intensity of connection between its relevant internal states can be adjusted.
Voice learning allows an action desired by a user to manifest itself among a set of similar actions determined in regard to user's arbitrary voice commands. The voice learning can teach behavior suitable for arbitrary commands among all actions, gradually decreasing a set of actions which becomes a learning goal, and reinforcing learning of results of actions by each set of similar actions. For example, a set of actions similar to ‘Sit down’ includes ‘sitting’, ‘crouching’ and ‘lying down’, and a set of actions similar to ‘Come here’ includes ‘pursuing’, ‘approaching’, ‘kicking’ and ‘touching.’
Since, in the learning function of the abovementioned prior software robot, behavior taught to the software robot is confined to a set of similar actions, and the software robot can be taught only some specific actions. Also, during reinforcement learning, a user had to give the software robot a reward or penalty for its behavior one by one. By using the learning methods of the prior art, learning emotions and motivation could be accomplished, but learning how to maintain homeostasis could not be attained.